Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty

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dc.article.numbere2316616121
dc.catalogadorgjm
dc.contributor.authorBaboun Larach, José
dc.contributor.authorBeaudry, Isabelle S.
dc.contributor.authorCastro Cepero, Luis Mauricio
dc.contributor.authorGutiérrez González, Felipe Iván
dc.contributor.authorJara Vallejos, Alejandro Antonio
dc.contributor.authorRubio Orellana, Benjamín Eduardo
dc.contributor.authorVerschae, José
dc.date.accessioned2024-06-26T19:58:28Z
dc.date.available2024-06-26T19:58:28Z
dc.date.issued2024
dc.description.abstractMotivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.
dc.fechaingreso.objetodigital2024-09-03
dc.format.extent10 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1073/pnas.2316616121
dc.identifier.issn1091-6490
dc.identifier.scopusidSCOPUS_ID:85189720608
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86872
dc.information.autorucEscuela de Ingeniería; Baboun Larach, José; 0009-0005-7269-1087; 1045055
dc.information.autorucFacultad de Matemáticas; Castro Cepero, Luis Mauricio; 0000-0001-7249-5207; 151425
dc.information.autorucEscuela de Ingeniería; Gutiérrez González, Felipe Iván; 0009-0000-5718-6967; 186172
dc.information.autorucFacultad de Matemáticas; Jara Vallejos, Alejandro Antonio; 0000-0002-2282-353X; 127927
dc.information.autorucEscuela de Ingeniería; Rubio Orellana, Benjamín Eduardo; S/I; 1026156
dc.information.autorucFacultad de Matemáticas; Verschae, José; 0000-0002-2049-6467; 243006
dc.issue.numero14
dc.language.isoen
dc.nota.accesocontenido parcial
dc.revistaProceedings of the National Academy of Sciences of the United States of America
dc.rightsacceso restringido
dc.subjectPublic health surveillance systems
dc.subjectRobust algorithms
dc.subjectSearch in uncertain trees
dc.subjectWastewater-based epidemiology
dc.titleIdentifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty
dc.typeartículo
dc.volumen121
sipa.codpersvinculados1045055
sipa.codpersvinculados151425
sipa.codpersvinculados186172
sipa.codpersvinculados127927
sipa.codpersvinculados1026156
sipa.codpersvinculados243006
sipa.trazabilidadORCID;2024-06-24
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